“How do we do that?” An analysis of TikToks by lesbians over age 30 representing sexual identity, lived experience over time, and solidarity
Bibliographic record
Abstract
Lesbians have long turned to digital media and technologies for information, support, and to self-represent sexual identity in ways that have the capacity for building communities and gathering publics and counterpublics. TikTok is a short video platform popular with young people, which has increasingly seen the participation of comparatively older users. This paper investigates the self-representation of lesbians over age 30 on TikTok to understand the themes in their content and how the platform shapes their communication with others. Through sampling tailored to TikTok's algorithmic curation, ten lesbians' accounts are examined alongside qualitative coding and analysis of 50 of these creators' videos. Findings reveal key themes regarding the expression of identity and age, lived experience over time, and bids for connection and community. TikTokers expressed lesbian identity in continuity with longstanding stereotypes to enhance visibility but also incorporated humor and youthful trends to give rise to novel identity expressions. Videos showcasing the passage of time and sociopolitical change demonstrated the resilience of lesbian lives and conveyed hope while advice and statements of solidarity expressed support for young people's present struggles with homophobia and transphobia. Contrasting with studies of TikTok's generational wars, this article shows how older lesbians are building generational bridges through their uptake of youth-driven platform practices, sharing of past challenges to support youth in overcoming present hurdles, and by modeling lesbian futures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".